1. Field of the Invention
The invention is a method and apparatus for qualifying an object.
2. Discussion of the Background
By plotting alterations of a physical characteristic measurable on a material as a function of another physical characteristic a spectrum is obtained. A spectrum is obtained for example, when an optical characteristic (transmission or reflection) of a material is plotted as a function of the wavelength of the illuminating radiation. Spectra are useful information sources relating to qualities, e.g. composition, of a material examined. However, the relationship between data representing qualities of the examined material and a certain spectrum of the sample cannot be described today in most cases in the form of mathematical equations. Therefore, determining relationships in an empirical way is relied upon. For this, a large number of samples with known qualities are required and spectra of all samples must be measured. In present day measurement technology it is not rare that a single spectrum is represented by a thousand spectrum values. The processing of the spectrum means that operations are to be carried out with these one thousand data.
Obtaining and interpreting useful information require in most cases the application of complicated methods of mathematical statistics. Even the majority of such spectrum recording and evaluating instruments and methods become suitable for determining qualities of samples of unknown composition from their spectra after a "learning" process of a large number of measurements (calibration) carried out on a series of samples of known qualities. Of methods of mathematical statistics applied for processing spectra, a relatively simple and widely applied method is the MLR (Multiple Linear Regression) method. Good results can be achieved by the MLR method, however, it has the disadvantage that only spectrum values associated with some discrete values of independent variable are used in determining the quality or qualities sought, although other spectrum values also carry valuable information. Further, when the program is run on a PC configuration, the calculation period may be rather long.
Shortening of the calculation period and the learning process may be achieved by transforming spectra into Fourier domain and taking into consideration the first 20 to 50 members of the Fourier series, only. This method represents a substantial data reduction, generally by one order of magnitude, in a way that in determining the reduced data all original spectrum data play a role. A spectral pattern classification system using discrete Fourier transformation is described in U.S. Pat. No. 4,783,754. The signal to be classified is sampled and the samples are multiplied by weighting constants inherent in the system prior to performing discrete Fourier transformation. The preprocessing ensures that data blocks from similar sources will have spectra that are close to one another in the Fourier domain.
A method different from those above, implementing a large-scale data reduction based on geometry, has also been suggested by K. J. Kaffka and L. S. Gyarmati in an article entitled Qualitative (Comparative) Analysis by Near Infrared Spectroscopy, Proceedings of the Third International Conference on Near Infrared Spectroscopy, Jun. 25-29, 1990, Brussels, Belgium, pp. 135-144. According to the method, spectrum values measured are not plotted in a usual rectangular coordinate system, but in a polar coordinate system, hypothetical masses of equal amount are assigned to the spectrum points so obtained, and their center of mass, that is the center of gravity is determined. The polar coordinate system was called "quality plane", the center of gravity was named "quality point" and the vector drawn from the origin of the coordinate system to the quality point was called "quality vector". According to this method, the quality, e.g. composition, of materials that can be characterised by their spectra can be described by the quality point or quality vector, respectively.
In many cases, however, the examined object is not homogeneous, and the distribution of various components is not uniform in the object. At the same time, knowledge about planar (spatial) distribution of the components could be important e.g. from the aspect of quality. To this end, elaboration of such a special technique is required, which enables measurement of reflection spectra of objects in two dimensions. The size of the object could vary between wide limits. The inhomogeneous surface could be of one cm.sup.2 or smaller size, e.g. when examining pharmaceuticals, cosmetics or foodstuff, it could be of some dm.sup.2 size, e.g. when protein or fat distribution of a body part is to be determined for medical purposes, but it could also be of several thousand km.sup.2 size, e.g. when grain crops of a country are to be predicted on the basis of aerial photographs taken at different wavelengths about the arable land. The solution is to project the surface of the object to be examined to a photosensitive matrix.
According to the publication of S. K. Taylor and W. F. McClure: NIR Imaging Spectroscopy: Measuring the Distribution of Chemical Components, Proceedings of the 2nd International NIRS Conference, Tsukuba, Japan, 1989, pp. 393-404, a photosensitive camera including a charge coupled device (CCD) as an image detector was used to distinguish healthy and dead tissue parts of tree leaves, with 320*240 detector elements (pixels) which could distinguish 256 light intensity levels. On one wavelength this represents 76,800 eight-bit data. In order to obtain reliable physical-chemical information from the intensity data measured on the pixels, the measurement should be carried out on several hundred wavelengths. The large quantity of data resulting would exceed the data processing possibilities of a normal PC. Even in the method mentioned above, measurement was carried out on six wavelengths by changeable filters installed in a rotating disk.
In the publication of P. Robert, D. Bertrand, M. F. Devaux and A. Sire: Identification of Chemical Constituents by Multivariate Near-Infrared Spectral Imaging, Analytical Chemistry, Vol. 64, No. 6, Mar. 15, 1992, pp. 664-667, measurements carried out on grain by a NIR video camera with 512*512 pixels, on 21 wavelength values are described. The pixels could distinguish 256 light intensity stages. In order to reduce the noise eight pictures were averaged and in order to facilitate data processing signals of four neighbouring pixels were averaged.